• Journal of Mechanical Science and Technology
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• v.14 no.7
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• pp.730-736
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• 2000

In this paper, several tire models (Magic formula, Carpet plot, VA tire, DADS tire and STI tire) are implemented and compared. Since the STI (System Technology Inc.) tire model in the AutoDyn7 program is in a good agreement to NADSdyna STI tire model and experiment, it is selected as a reference tire model for the comparison. To compare tire models, input parameters of each tire model are extracted from the STI tire model to preserve the same tire properties. Several simulations are carried out to compare performances of tire models, i. e., bump simulation, lane change simulation, and pulse steering simulation. The performances in vehicle maneuverability are also compared with the four parameter evaluation method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.11
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• pp.1813-1821
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• 2004

Until now, the 40% of wall thickness criterion, which is generally used for the plugging of steam generator tubes, has been applied only to a single cracked geometry. In the previous study by the authors, a total number of 9 local failure prediction models were introduced to estimate the coalescence load of two collinear through-wall cracks and, then, the reaction force model and plastic zone contact model were selected as the optimum ones. The objective of this study is to estimate the coalescence load of two collinear through-wall cracks in steam generator tube by using the optimum local failure prediction models. In order to investigate the applicability of the optimum local failure prediction models, a series of plastic collapse tests and corresponding finite element analyses for two collinear through-wall cracks in steam generator tube were carried out. Thereby, the applicability of the optimum local failure prediction models was verified and, finally, a coalescence evaluation diagram which can be used to determine whether the adjacent cracks detected by NDE coalesce or not has been developed.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1567-1576
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• 2023

The reactor containment building (RCB) in nuclear power plants (NPPs) plays an important role in protecting the reactor systems from external loads as well as preventing radioactive leaking. As we witnessed the nuclear disaster at Fukushima Daiichi (Japan) in 2011, the earthquake is one of the major threats to NPPs. The purpose of this study is to evaluate effects of concrete material models and presstressing forces on the seismic performance evaluation of RCB in NPPs. A typical RCB designed in Korea is employed for a case study. Detailed three-dimensional nonlinear finite element models of RCB are developed in ANSYS. A series of pushover analyses are then performed to obtain the pushover curves of RCB. Different capacity curves are compared to recognize the influence of different material models on the nonlinear behavior of RCB. Additionally, the effects of prestressing forces on the seismic performances of the structure are also investigated. Moreover, a set of damage states corresponding to damage evolutions of the structures is proposed in this study.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.1-13
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• 2023

As interest in urban safety has increased since COVID-19, various institutions have developed and used indicators that evaluate the safety city model. Yongsan-gu was ranked No. 1 in 2021 by Social Safety Index evaluation and was selected as the safest city in Korea. However, the Itaewon disaster in Yongsan-gu in 2022 caused many casualties. The study of indicators for evaluating cities' safety was necessary. This study aims to examine domestic and foreign safe city models and review the differences between each model and the indicators used to evaluate safe cities. As a result of collecting 11 safe city models and analyzing each evaluation index, safe city models can be classified into program-based safe city models, such as the World Health Organization's International safe community and the UN Office for Disaster Risk Reduction's International Safe city. Considering the diversification of threats to safety, it is reasonable to comprehensively consider digital security, health safety, infrastructure safety, personal safety, environmental safety, traffic safety, fire safety, crime safety, life safety, suicide, and infectious diseases when evaluating safe cities as evaluation parameters.

• Journal of Information Technology Applications and Management
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• v.21 no.4_spc
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• pp.403-414
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• 2014

Forecasting technology in economic activity is a quite intricate procedure so researchers should grasp the point of the data to use. Diffusion models have been widely used for forecasting market demand and measuring the degree of technology diffusion. However, there is a question that a model, explaining a certain market with goodness of fit, always shows good performance with markets of different conditions. The primary aim of this paper is to explore diffusion models which are frequently used by researchers, and to help readers better understanding on those models. In this study, Logistic, Gompertz and Bass models are used for forecasting Global Wireline Subscribers and the performance of models is measured by Mean Absolute Percentage Error. Logistic model shows better MAPE than the other two. A possible extension of this study may verify which model reflects characteristics of industry better.

• ETRI Journal
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• v.21 no.3
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• pp.6-15
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• 1999

Many studies in the software reliability have attempted to develop a model for predicting the faults of a software module because the application of good prediction models provides the optimal resource allocation during the development period. In this paper, we consider the change request data collected from the field test of the software module that incorporate a functional relation between the faults and some software metrics. To this end, we discuss the general aspect if regression method, the problem of multicollinearity and the measures of model evaluation. We consider four possible regression models including two stepwise regression models and two nonlinear models. Four developed models are evaluated with respect to the predictive quality.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.99-104
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• 2006

The new gravity field combination models are expected to improve the knowledge of the Earth's global gravity field. This study evaluates eleven global gravity field models derived from gravimetry and altimetry surface data in a comparison with ground truth in South Korea. Geoid heights obtained from GPS and levelling in South Korea are compared with geoid heights from the models. The results show that the gravity satellites CHAMP, GRACE and LAGEOS plus gravimetry and altimetry surface data have led to an improvement in gravity field models. As expected, the new combination gravity field model which are EIGEN-CG03C and EIGEN-GL04C give better results than the predecessors widely used models(EGM96, OSU91A etc.).

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.219-227
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• 2007

The time series models have been used to analyze and predict the network traffic. In this paper, we compare the performance of the time series models for prediction of network traffic. The feasibility study showed that a class of nonlinear time series models can be outperformed than the linear time series models to predict the network traffic.

• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.367-370
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• 2006

Performances of multiple surrogate models are evaluated in a turbomachinery blade shape optimization. The basic models, i.e., Response Surface Approximation, Kriging and Radial Basis Neural Network models as well as weighted average models are tested for shape optimization. Global data based errors for each surrogates are used to calculate the weights. These weights are multiplied with the respective surrogates to get the final weighted average models. The design points are selected using three level fractional factorial D-optimal designs. The present approach can help address the multi-objective design on a rational basis with quantifiable cost-benefit analysis.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.997-1002
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• 1994

Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. Those models can effectively normalize the temporal and spatial variability of speech signals. But those models suffer from poor discrimination between acoustically similar words. In this paper, we propose a discriminative training algorithm for predictive neural network models based on a generalized probabilistic descent (GPD) algorithm and minimum classification error formulation (MCEF). The Evaluation of our training algorithm on ten Korean digits shows its effectiveness by 40% reduction of recognition error.